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Functional Transcranial Doppler: selection of methods for statistical analysis and representation of changes in flow velocity.
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  • Stephan Egger,
  • Julio Bobes,
  • Erich Seifritz,
  • Daniel Schuepbach
Stephan Egger
University of Zurich Faculty of Medicine

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Julio Bobes
University of Oviedo
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Erich Seifritz
University of Zurich Faculty of Medicine
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Daniel Schuepbach
University of Heidelberg
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Introduction Transcranial Doppler (TCD) is method used to study cerebral hemodynamics. The analysis majority of TCD studies are conducted at group level, pooling change in flow velocity over several subjects. Analysis of variance are the most frequently applied statistical methods However, due to the dynamic nature of flow velocity, non- parametric tests, may allow for a better representation of results. Methods During a visuo-motor task, the mean flow velocity (MFV) in the middle cerebral artery (MCA) in healthy-subjects was measured using TCD. All MFV values were converted to relative values, i.e. compared with resting values. The results obtained were analyzed using the general linear model (GLM) and the general additional models (GAM). Both methods of analysis were compared against with each other. Results The sample comprised 30 healthy participants, aged 33.87±7.48 years; 33% females. The MFV for the first 20 seconds was 1.06±0.07 in the right-MCA and 1.08±0.07 in the left-MCA. Both MCAs showed a steady increase in MFV, returning to resting state. GL- and GA-Models showed a statistically significant change in MFC (GLM: F (2, 3598) = 16.76, p<0.001; GAM: F (2, 3598) = 21.63, p<0.001); as well as differences in hemispheric side and gender. Comparing the models using a Chi-square test for goodness of fit yields a significant difference X2 (9.9556) = 0.6836, p= <0.000. With a superiority of the models using GAM. Discussion GLM and GAM of the MFV yielded similar results; the model using the GAM resulted in a better measurement of fit. The GAM’s advantage becomes clearer when the mean flow velocity curves are visualised; yielding a more realistic approach to brain hemodynamics, thus allowing for an improvement in interpretation of the mathematical and statistical results. Conclusion Our results demonstrate the utility of the GAM for the analysis of hemodynamic measurements.